Evidence map›Paper›PMID 41691972›Full record

ArticleClinics (Sao Paulo, Brazil)2026

Personalized sepsis mortality prediction: An interpretable machine learning nomogram.

Lulu Weng, Haidong Li, Yonglai Lv, Jiayi Luo, Zhenliang Wen, Jiawen Shi, Li Zhong

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Article in Clinics (Sao Paulo, Brazil), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Lulu WengDepartment of Critical Care Medicine, First Affiliated Hospital of Huzhou University, the First People's Hospital of Huzhou, Zhejiang, China.
Haidong LiDepartment of Spine Surgery, First Affiliated Hospital of Huzhou University, the First People's Hospital of Huzhou, Zhejiang, China.
Yonglai LvDepartment of Critical Care Medicine, First Affiliated Hospital of Huzhou University, the First People's Hospital of Huzhou, Zhejiang, China.
Jiayi LuoFaculty of Engineering, Universiti Malaya, Wilayah Persekutuan Kuala Lumpur, Malaysia.
Zhenliang WenDepartment of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China.
Jiawen ShiDepartment of Internal Medicine, Second People's Hospital of Anji County, Zhejiang, China.
Li ZhongDepartment of Critical Care Medicine, First Affiliated Hospital of Huzhou University, the First People's Hospital of Huzhou, Zhejiang, China. Electronic address: zhongli@zjhu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeSepsis remains a major cause of mortality in ICU patients, requiring accurate prognostic tools for optimal management. This study aimed to develop and validate an interpretable machine learning-based nomogram for predicting in-hospital mortality in sepsis patients to guide clinical decision-making.

methodsThis retrospective cohort study included 407 adult sepsis patients from ICU admissions between January 2019 and December 2024. Patients were randomly divided into training (n = 284) and validation (n = 123) cohorts. A LASSO-based multivariate logistic regression was applied to construct a predictive nomogram.

resultsSeven independent predictors emerged for in-hospital mortality: age, abdominal infection, vasopressor requirement, WBC, BNP, APACHE II score, and mechanical ventilation support. The nomogram showed strong discrimination with an AUC of 0.900 in the training cohort and 0.796 in the validation cohort. Calibration curves, decision curve analysis, and SHAP interpretation confirmed good model performance and clinical utility.

conclusionsThe novel machine learning-based nomogram provides a practical, interpretable risk assessment tool for early mortality prediction, potentially improving patient outcomes through enhanced clinical understanding and timely interventions in critically ill sepsis patients.

Indexed as

Intensive care unitMortality predictionNomogramPrediction modelRisk stratificationSHAP

Identifiers

PMID41691972
PMCPMC12924733

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